Conditional maximum entropy modeling has been utilized in several natural language processing applications that aim at modeling the conditional distribution of a class given an observation. Existing techniques assume the input or observations are deterministic. In certain real-life applications, however, such as spoken dialog systems, and speech to speech translation, observations may be uncertain and subject to speech recognition errors.
Conditional maximum entropy models have also been applied to natural language processing applications that directly work on text as observation. However, in certain applications, such as spoken dialog systems or speech to speech translation, observations are usually uncertain, and one may need to rely on non-perfect preprocessing methods, which are subject to error. Past works have ignored uncertainty in the observations and have assumed the observation is correct.
Errors in speech recognizer output may pose a problem for classification tasks, which are based on observation, including, for example, such classification tasks as topic classification, automatic question answering, spoken dialog systems, etc. In particular, erroneous observations may pose a problem for devices such as human-machine interaction systems, which are required to respond under noisy conditions. In this context, the traditional methods are vulnerable to noisy observations, which may occur, for example, when people drive a car.